Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes

Publication Type

Journal Article

Publication Date

1-2019

Abstract

In this paper, we propose a privacy-preserving reinforcement learning framework for a patient-centric dynamic treatment regime, which we refer to as Preyer. Using Preyer, a patient-centric treatment strategy can be made spontaneously while preserving the privacy of the patient's current health state and the treatment decision. Specifically, we first design a new storage and computation method to support noninteger processing for multiple encrypted domains. A new secure plaintext length control protocol is also proposed to avoid plaintext overflow after executing secure computation repeatedly. Moreover, we design a new privacy-preserving reinforcement learning framework with experience replay to build the model for secure dynamic treatment policymaking. Furthermore, we prove that Preyer facilitates patient dynamic treatment policymaking without leaking sensitive information to unauthorized parties. We also demonstrate the utility and efficiency of Preyer using simulations and analysis.

Keywords

Cryptography, Diseases, Dynamic Treatment Regime, Experience Replay, Patient-Centric, Privacy, Privacy-Preserving, Protocols, Q-learning, Reinforcement learning, Reinforcement Learning, Cloud computing, Computational modeling

Discipline

Health Information Technology | Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Emerging Topics in Computing

ISSN

2168-6750

Identifier

10.1109/TETC.2019.2896325

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TETC.2019.2896325

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